A0169
Title: Some contributions to harmonizable time series analysis
Authors: Jean-Marc Freyermuth - Aix-Marseille University (France) [presenting]
Anna Dudek - AGH University of Krakow (Poland)
Dominique Dehay - University of Rennes (France)
Abstract: Harmonizable time series are natural extensions of stationary time series with a spectral decomposition whose components are correlated. Thus, the covariance function of a harmonizable time series is bivariate and admits a two-dimensional Fourier decomposition (Loeve spectrum). They form a broad class of nonstationary processes that has been a subject of investigation for a long time. We introduce a parametric form for these harmonizable processes, namely Harmonizable Vector AutoRegressive and Moving Average models (HVARMA), and we give tools to generate finite time sample realizations of HVARMA with known Loeve spectrum. Then, we discuss nonparametric estimation of spectral characteristics of spatiotemporal processes that are locally time-harmonizable, and illustrate its application in EEG data analysis.
